BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition

Understand

Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification.

• Despite the success, the huge number of parameters blocks its deployment to situations with light computing resources.
• Researchers resort to the redundancy in the weights of DNNs and attempt to find how fewer parameters can be chosen while preserving the accuracy at the same time.
• Although several promising results have been shown along this research line, most existing methods either fail to significantly compress a well-trained deep network or require a heavy fine-tuning process for the compressed network to regain the original performance.